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A Cool Brisk Walk Through Discrete Mathematics A Cool Brisk Walk Through Discrete Mathematics version 1.4 Stephen Davies, Ph.D. Computer Science Department University of Mary Washington 1 Copyright c 2019 Stephen Davies. University of Mary Washington Department of Computer Science Trinkle Hall 1301 College Avenue Fredericksburg, VA 22401 Permission is granted to copy, distribute, transmit and adapt this work under a Creative Commons Attribution-ShareAlike 4.0 Inter- national License: http://creativecommons.org/licenses/by-sa/4.0/ The accompanying materials at www.allthemath.org are also un- der this license. If you are interested in distributing a commercial version of this work, please contact the author at [email protected]. The LATEXsource for this book is available from: https://github. com/rockladyeagles/cool-brisk-walk. Cover art copyright c 2014 Elizabeth M. Davies. Contents at a glance Contents at a glance i Preface iii 1 Meetup at the trailhead 1 2 Sets 7 3 Relations 35 4 Probability 59 5 Structures 85 6 Counting 133 7 Numbers 153 8 Logic 181 9 Proof 203 Also be sure to check out the forever-free-and-open-source instructional videos that accompany this series, at www.allthemath.org! i Preface Discrete math is a popular book topic | start Googling around and you'll find a zillion different textbooks about it. Take a closer look, and you'll discover that most of these are pretty thick, dense volumes packed with lots of equations and proofs. They're prin- cipled approaches, written by mathematicians and (seemingly) to mathematicians. I speak with complete frankness when I say I'm comforted to know that the human race is well covered in this area. We need smart people who can derive complex expressions and prove theorems from scratch, and I'm glad we have them. Your average computer science practitioner, however, might be bet- ter served by a different approach. There are elements to the dis- crete math mindset that a budding software developer needs ex- perience with. This is why discrete math is (properly, I believe) part of the mandatory curriculum for most computer science un- dergraduate programs. But for future programmers and engineers, the emphasis should be different than it is for mathematicians and researchers in computing theory. A practical computer scientist mostly needs to be able to use these tools, not to derive them. She needs familiarity, and practice, with the fundamental concepts and the thought processes they involve. The number of times the average software developer will need to construct a proof in graph theory is probably near zero. But the times she'll find it useful to reason about probability, logic, or the properties of collections are frequent. I believe the majority of computer science students benefit most from simply gaining an appreciation for the richness and rigor of iii iv PREFACE this material, what it means, and how it impacts their discipline. Becoming an expert theorem prover is not required, nor is deriving closed-form expressions for the sizes of trees with esoteric proper- ties. Basic fluency with each topic area, and an intuition about when it can be applied, is the proper aim for most of those who would go forward and build tomorrow's technology. To this end, the book in your hands is a quick guided tour of introductory-level discrete mathematics. It's like a cool, brisk walk through a pretty forest. I point out the notable features of the landscape and try to instill a sense of appreciation and even of awe. I want the reader to get a feel for the lay of the land, and a little exercise. If the student acquires the requisite vocabulary, gets some practice playing with the toys, and learns to start thinking in terms of the concepts here described, I will count it as a success. Chapter 1 Meetup at the trailhead Before we set out on our \cool, brisk walk," let's get oriented. What is discrete mathematics, anyway? Why is it called that? What does it encompass? And what is it good for? Let's take the two words of the subject, in reverse order. First, math. When most people hear \math," they think \numbers." After all, isn't math the study of quantity? And isn't that the class where we first learned to count, add, and multiply? Mathematics certainly has its root in the study of numbers | specifically, the \natural numbers" (the integers from 1 on up) that fascinated the ancient Greeks. Yet math is broader than this, al- most to the point where numbers can be considered a special case of something deeper. In this book, when we talk about trees, sets, or formal logic, there might not be a number in sight. Math is about abstract, conceptual objects that have prop- erties, and the implications of those properties. An \object" can be any kind of \thought material" that we can define and reason about precisely. Much of math deals with questions like, \suppose we defined a certain kind of thing that had certain attributes. What would be the implications of this, if we reasoned it all the way out?" The \thing" may or may not be numerical, whatever it turns out to be. Like a number, however, it will be crisply defined, have certain known aspects to it, and be capable of combining with other things in some way. 1 2 CHAPTER 1. MEETUP AT THE TRAILHEAD Fundamental to math is that it deals with the abstract. Abstract, which is the opposite of concrete, essentially means something that can't be perceived with the senses. A computer chip is concrete: you can touch it, you can see it. A number is not; nor is a function, a binary tree, or a logical implication. The only way to perceive these things is with the power of the mind. We will write expres- sions and draw pictures of many of our mathematical structures in order to help visualize them, and nearly everything we study will have practical applications whereby the abstractness gets grounded in concreteness for some useful purpose. But the underlying math- ematical entity remains abstract and ethereal | only accessible to the mind's eye. We may use a pencil to form the figure \5" on a piece of paper, but that is only a concrete manifestation of the underlying concept of “five-ness." Don't mistake the picture or the symbol for the thing itself, which always transcends any mere physical representation. The other word in the name of our subject is \discrete" (not to be confused with \discreet," which means something else entirely). The best way to appreciate what discrete means is to contrast it with its opposite, continuous. Consider the following list: Discrete Continuous whole numbers (Z) real numbers (R) int double digital analog quantum continuum counting measuring number theory analysis R Σ d { dx What do the left-hand entries have in common? They describe things that are measured in crisp, distinct intervals, rather than varying smoothly over a range. Discrete things jump suddenly from position to position, with rigid precision. If you're 5 feet tall, you might some day grow to 5.3 feet; but though there might be 5 3 people in your family, there will never be 5.3 (although there could be 6 someday). The last couple of entries on this list are worth a brief comment. They are math symbols, some of which you may be familiar with. R d On the right side | in the continuous realm | are and dx , which you'll remember if you've taken calculus. They stand for the two fundamental operations of integration and differentiation. Integration, which can be thought of as finding \the area under a curve," is basically a way of adding up a whole infinite bunch of numbers over some range. When you \integrate the function x2 from 3 to 5," you're really adding up all the tiny, tiny little vertical slivers that comprise the area from x = 3 on the left to x = 5 on the right. Its corresponding entry in the left-column of the table is Σ, which is just a short-hand for \sum up a bunch of things." Integration and summation are equivalent operations, it's just that when you integrate, you're adding up all the (infinitely many) slivers across the real-line continuum. When you sum, you're adding up a fixed sequence of entries, one at a time, like in a loop. R Σ is just the discrete \version" of . The same sort of relationship holds between ordinary subtraction d (\{") and differentiation ( dx ). If you've plotted a bunch of discrete points on x-y axes, and you want to find the slope between two of them, you just subtract their y values and divide by the (x) distance between them. If you have a smooth continuous function, on the other hand, you use differentiation to find the slope at a point: this is essentially subtracting the tiny tiny difference between two supremely close points and then dividing by the distance between d them. Thus subtraction is just the discrete \version" of dx . Don't worry, you don't need to have fully understood any of the integration or differentiation stuff I just talked about, or even to have taken calculus yet. I'm just trying to give you some feel for what \discrete" means, and how the dichotomy between discrete and continuous really runs through all of math and computer sci- ence. In this book, we will mostly be focusing on discrete values and structures, which turn out to be of more use in computer sci- ence.
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